The present embodiments relate to spectrophotometer scanning systems particularly suitable for high speed online document color analysis. Such systems must be calibrated and characterized in accordance with the particular operating characteristics of component illumination sources (LEDs) and light reflectance sensors (photodiodes). A reference database cognizant of the particular performance of each sensor in the system is usually utilized for such characterization purposes. The present embodiments especially relate to the formations of such reference databases.
Spectroscopy is the measurement and analysis of electromagnetic radiation absorbed, scattered, or emitted by atoms, molecules, or other chemical or physical materials. Each object affects light in its own unique way. When light waves strike an object, the object's surface absorbs some of the spectrum's energy, while other parts of the spectrum are reflected back from the object. The modified light that is reflected from the object has an entirely news composition of wavelengths. Different surfaces containing various pigments, dyes, and inks generate different but unique wavelength compositions. Light can be modified by striking a reflective object such as paper; or by passing through a transmissive object such as film or a transparency. The pattern of wavelengths that leaves an object is the object's spectral data, which is often called the “finger print” of the object. A typical spectrophotometric system measures the electromagnetic spectrum from 380 nm to 730 nm or so, to cover the humanly visible color spectra or wavelength range, but it can be used to take spectra measurements of colors that cannot be seen by the eye (UV, infrared) which when considered in calibrating the sensor can be useful for even more accurate calibration.
Automatic inline color calibration systems can be much more effective with an inline color measurement system where a spectrophotometer may be mounted in the paper path of the moving copy sheets in the printer, preferably in the output path after fusing or drying, without having to otherwise modify the printer, or interfere with or interrupt normal printing, or the movement of the printed sheets in said paper path, and yet provide accurate color measurements of test color patches printed on the moving sheets as they pass the spectrophotometer. That enables a complete closed loop color control of a printer.
As used herein, unless otherwise specifically indicated, the term “spectrophotometer” may encompass a spectrophotometer, calorimeter, and densitometer, as broadly defined herein.
Sensor-to-sensor inaccuracies occur largely due to differences in LED emission curves; their peak LED wavelengths and full width half max values in the emission spectra; and, it is often difficult to find similar LEDs within a batch. Also, when a sensor is installed on a printer, it may require personalization to improve accuracy due to variations in mounting, ambient and mechanical tolerances. In Table 1 below, some examples of differences between LEDs measured in terms of deltaE between two emission curves of the same type of sensors are shown. For example, when 505 nm LEDs comprise the illumination source, there is a difference in sensed measurement between sensor head 40 and 41 of 10.84 deltaE. The sensors are identified by head numbers. DeltaE is the unit that is normally used to measure the color consistency. It is the color difference unit that uses the Euclidian distance norm to determine the difference between two colors in the color gamut being considered. In ClELab, deltaE equal to one is an indication of high quality print (a just noticeable difference.) In a truly uniform color space, one color difference unit would correspond to the same perceived difference between the two colors. A value of less than 3 deltaE is considered the maximum threshold value that cannot be exceeded for good color quality. Similarly, 470 nm LED and 530 nm LEDs have differences of 43.61 deltaE and 26.63 deltaE respectively. These differences give rise to large variations in accuracy in measurement accuracy.
TABLE 1-nms35-s41s40-s41s42-s41s38-s41s39-s41s38-s39s40-s424305.996.5712.4927.658.6521.5317.8447043.618.245.706.164.399.1613.895057.7310.843.709.1910.412.3410.9853026.6310.812.632.4011.9110.859.225653.736.343.813.235.502.4610.105902.912.212.181.421.502.583.656203.925.232.543.944.660.777.746602.113.431.982.394.051.825.37
With reference to FIG. 1, comprising a schematic representation of differences in accuracy between selected sensors identified in TABLE 1 and contigously disposed in a spectrophotometric system, it can be seen that sensor 35 measurements are more inaccurate than sensor 41. This difference is due to the large differences in emission spectra from the 470 nm and 530 nm LEDs. Also, the 505 nm and 530 nm LEDs have contributed for sensor 40 errors. In sensor 35, patches with blue spectral content (e.g., cyan patches with magenta, yellow and black set to zero) are illuminated with LEDs with different emission spectra than sensor 41 due to about a 10 nm peak wavelength shift in the blue LED of sensor 35 compared to the blue LED of sensor 41, which can give rise to such measurement errors for blue colors. Given these exemplary problems, one conventional way to correct the problem is by measuring all of the training samples by each sensor during the sensor characterization process and then building therefrom, customized spectral reconstruction matrices for each individual sensor. Unfortunately, the number of training samples used during characterizations for such sensors is very large, e.g. approximately 3000 colors. Such an extensive characterization process would require measuring all the training sample colors by each sensor in the factory/or on the system, which is a highly undesirable solution in terms of the time and cost for completion of such a task.
There is substantial need for an alternative sensor characterization method in which spectral reconstruction matrices are personalized to an acceptable standard of accuracy using a much reduced set and number of training sample colors for each sensor, which can then drastically reduce the time required to customize the reference databases for each sensor.